TimeSeries Class 
Namespace: Meta.Numerics.Statistics
The TimeSeries type exposes the following members.
Name  Description  

TimeSeries 
Initializes a new, empty time series.
 
TimeSeries(Double) 
Initializes a new time series with the given values.

Name  Description  

Count 
Gets the number of points in the time series.
 
Item 
Gets or sets the value of the time series at a given index.
 
Mean 
Gets the mean of the time series.

Name  Description  

Add(Double) 
Adds a point to the time series.
 
Add(Double) 
Adds multiple points to the time series.
 
AsSample 
Gets a sample containing the timeseries values.
 
Autocovariance 
Computes the autocovariance for all lags.
 
Autocovariance(Int32) 
Computes the autocovariance of the series at the given lag.
 
Clear 
Removes all points from the time series.
 
Contains 
Determines whether the time series contains a value.
 
Difference 
Recomputes the time series as the differences between sequential values of the original series.
 
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
FitToAR1 
Fits an AR(1) model to the time series.
 
FitToMA1 
Fits an MA(1) model to the time series.
 
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
IndexOf 
Finds the index at which a value occurs.
 
Integrate 
Recomputes the time series as the sums of sequential values of the original series.
 
LjungBoxTest 
Performs a LjungBox test for noncorrelation.
 
LjungBoxTest(Int32) 
Performs a LjungBox test for noncorrelation with the given number of lags.
 
PopulationStatistics 
Computes estimates for the moments of the population from which the time series is drawn.
 
PowerSpectrum 
Computes the power spectrum of the time series.
 
ToString  Returns a string that represents the current object. (Inherited from Object.) 
Name  Description  

CentralMoment 
Computes the given sample central moment.
(Defined by Univariate.)  
CorrectedStandardDeviation 
Computes the Besselcorrected standard deviation.
(Defined by Univariate.)  
Maximum 
Finds the maximum value.
(Defined by Univariate.)  
Mean 
Computes the sample mean.
(Defined by Univariate.)  
Minimum 
Finds the minimum value.
(Defined by Univariate.)  
PopulationCentralMoment 
Estimates the given central moment of the underlying population.
(Defined by Univariate.)  
PopulationMean 
Estimates the mean of the underlying population.
(Defined by Univariate.)  
PopulationRawMoment 
Estimates the given raw moment of the underlying population.
(Defined by Univariate.)  
PopulationStandardDeviation 
Estimates of the standard deviation of the underlying population.
(Defined by Univariate.)  
PopulationVariance 
Estimates of the variance of the underlying population.
(Defined by Univariate.)  
RawMoment 
Computes the given sample raw moment.
(Defined by Univariate.)  
SignTest 
Tests whether the sample median is compatible with the given reference value.
(Defined by Univariate.)  
Skewness 
Computes the sample skewness.
(Defined by Univariate.)  
StandardDeviation 
Computes the sample standard deviation.
(Defined by Univariate.)  
StudentTTest 
Tests whether the sample mean is compatible with the reference mean.
(Defined by Univariate.)  
Variance 
Computes the sample variance.
(Defined by Univariate.)  
ZTest 
Performs a ztest to test whether the given sample is compatible with the given normal reference population.
(Defined by Univariate.) 